Equalizers are typically used in coded digital communications systems to compensate for multipath/linear filtering effects caused by the transmission channel. These effects are commonly referred to as channel impairments and include signal distortion which may occur in the transmitter, in the receiver or in the channel through which the signal is transmitted. The equalizer is an adaptive filter, often implemented as a finite impulse response (FIR) filter, an infinite impulse response (IIR) filter or a combination of FIR and IIR filters. Each filter has a plurality of coefficients which are adapted to minimize an error criterion. This error criterion may be, for example, the mean-square error between a transmitted training signal and the received training signal. A typical equalizer maintains a copy of the transmitted training signal to compare with the received training signal. It is generally believed that a decision-feedback equalizer (DFE) has better asymptotic performance than a linear equalizer as described in a text book by J. G. Proakis, entitled Digital Communications.
A typical DFE is shown in FIG. 1. The received signal is applied to an FIR filter and the output signal produced by the FIR filter is applied to an IIR filter. The IIR filter includes a subtracter 111, a slicer 112 and an IIR filter section 114. The subtracter 111 subtracts the filtered signal provided by the IIR filter section 114 from the output signal of the FIR filter 110. The slicer 112 quantizes the signal provided by the subtracter 111 to produce an approximation of the signal that was transmitted. The IIR filter section, which may, for example, be an FIR filter in a feedback loop, processes the quantized signal to produce the signal which is subtracted by the subtracter 111. For an uncoded modulation scheme, the DFE uses the slicer to get decisions for the feedback portion. The output signal of the slicer is compared to the training signal to determine in what way the coefficients of the FIR and IIR filter sections should be updated to minimize any differences.
For a coded modulation scheme, it may be desirable to replace the slicer with a decoder, which may include, for example, a trellis decoder, a deinterleaver, and a Reed Solomon (RS) decoder. Such a decoder, however, results in many symbol delays before a decision can be made on the symbol that was transmitted. These delays can be prohibitive for the DFE, since it relies on canceling the inter-symbol-interference of the previous symbols on the current symbol by using previously available decisions. Hence, the state of the art has typically not used a complete decoder, but a range of simplified decoders including the simple slicer 112, which does not perform any decoding. A typical problem with using only a slicer in a DFE is a loss in performance due to incorrect decisions. Because an incorrect decision used in the DFE to remove inter-symbol interference (ISI) can cause further errors, this performance loss is known as `error propagation`.
More complex decoding techniques may also be used, for example, Reduced-State Sequence Estimation (RSSE) and parallel decision feedback decoding (PDFD). These techniques are described in an article by V. Eyuboglu and S. Qureshi, entitled "Reduced-State Sequence Estimation for Coded Modulation on Intersymbol Interference Channels" IEEE Journal on Selected Areas of Communications, August 1989. Furthermore, U.S. Pat. No. 5,056,117 entitled DECISION-FEEDBACK EQUALIZATION WITH TRELLIS CODING to R. Gitlin, describes a method by which multiple possible decisions are fed back and the best among them is chosen using a given criteria. Other techniques are described in an article by A. Duel-Hallen and C. Heegard, entitled "Delayed Decision-Feedback Equalization", IEEE Transactions on Communications May 1989. All of the above cited references are incorporated herein by reference for their teachings on equalization and decoding techniques.
Generally the common idea among these decoders is to use multiple possible decisions or to use more complicated trellis decoders which include a channel state estimate. The implementation complexity of these approaches, however, is significant and may undesirably add to the cost of the decoder.